import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, association_rules
import matplotlib.pyplot as plt
import networkx as nx

df = pd.read_excel('D:/SPSS/餐厅数据.xlsx')

# 转换菜品数据格式
transactions = df['菜品'].str.split(',').to_list()

# 标准化数据
ts = TransactionEncoder()
te_ary = ts.fit(transactions).transform(transactions)
df_encoded = pd.DataFrame(te_ary, columns=ts.columns_)

# 使用apriori进行关联规则挖掘
frequent_itemsets = apriori(df_encoded, min_support=0.1, use_colnames=True)
frequent_itemsets.sort_values(by='support', ascending=False, inplace=True)

# 选择2项集查看（此步骤在代码中被注释掉了）
# print(frequent_itemsets[frequent_itemsets.itemsets.apply(lambda x: len(x)==2)])

rules = association_rules(frequent_itemsets, metric='confidence', min_threshold=0.1)
# 筛选有效规则
effective = rules[
    (rules['lift'] > 1) & (rules['confidence'] > 0.1)
].sort_values(by=['lift', 'confidence'], ascending=False)

print(effective[['antecedents', 'consequents', 'support', 'confidence', 'lift']])

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

# 关联关系可视化
G = nx.DiGraph()
for _, row in effective.iterrows():
    G.add_edge(
        ','.join(list(row['antecedents'])),
        ','.join(list(row['consequents'])),
        weight=row['lift']
    )

pos = nx.spring_layout(G)
plt.figure(figsize=(12, 8))
nx.draw(G, pos=pos,
        with_labels=True,
        edge_color=[G[u][v]['weight'] for u, v in G.edges()],
        width=2.0,
        edge_cmap=plt.cm.Blues)
plt.show()